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A novel soft sensor approach for estimating individual biomass in mixed cultures
Author(s) -
Stone Kyle A.,
Shah Devarshi,
Kim Min Hea,
Roberts Nathan R. M.,
He Q. Peter,
Wang Jin
Publication year - 2017
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.2453
Subject(s) - partial least squares regression , soft sensor , biological system , multivariate statistics , mixed model , line (geometry) , sample (material) , linear regression , computer science , range (aeronautics) , biochemical engineering , process engineering , statistics , mathematics , biology , chemistry , materials science , engineering , chromatography , process (computing) , geometry , composite material , operating system
Due to many advantages associated with mixed cultures, their application in biotechnology has expanded rapidly in recent years. At the same time, many challenges remain for effective mixed culture applications. One obstacle is how to efficiently and accurately monitor the individual cell populations. Current approaches on individual cell mass quantification are suitable for off‐line, infrequent characterization. In this study, we propose a fast and accurate “soft sensor” approach for estimating individual cell concentrations in mixed cultures. The proposed approach utilizes optical density scanning spectrum of a mixed culture sample measured by a spectrophotometer over a range of wavelengths. A multivariate linear regression method, partial least squares or PLS, is applied to correlate individual cell concentrations to the spectrum. Three experimental case studies are used to examine the performance of the proposed soft sensor approach. © 2017 American Institute of Chemical Engineers Biotechnol. Prog. , 33:347–354, 2017